Breast cancer, cervical cancer and PCOS (polycystic ovary syndrome) are the most common reproductive health issues found in women [1]. In an underdeveloped country like ours, women are under-privileged by the dominance of men. However, due to orthodox beliefs and lack of awareness women restrain themselves from going to the hospital for a proper checkup and discover their conditions only at the advanced stage. The main objective of this project is to bring all the common health problems of women into one web app and try to change the modality of health checkup and increase its awareness. This project helps to detect the presence of metastatic tissue and invasive ductal carcinoma in the breast by the help of histopathological images. Histopathological images are the images under the microscopic examination of a biopsy or surgical specimen that is processed and fixed onto glass slides. Traditional method of detecting breast cancer involves manual observation of pigmented tissues under microscope, which is a tedious and time-consuming task. It might also not always give accurate prediction. Hence, Convolutional Neural Network is used to extract features and then classify using a fully connected network. It also helps in detection of cervical cancer and PCOS in women by performing preliminary tests using Ensemble Classifier model, which combine the decisions from multiple models like decision tree, support vector machine (SVM), random forest, and logistic regression models to improve the overall performance. As a result, this app is useful for hospitals for the quick prediction of breast cancer, cervical cancer and PCOS with a maximum accuracy. This app has a user friendly UI design so it is useful for any one to predict if she has cervical cancer and PCOS. With this, women are able to detect it in an early stage and prevent further complications.